Research on Real-Time Local Rainfall Prediction Based on MEMS Sensors

Zeyi Chao,Xiaoling Chen,Fangling Pu,Yuke Yin,Bin Han

doi:10.1155/2018/6184713

Abstract

A more accurate and timely rainfall prediction is needed for flood disaster reduction and prevention in Wuhan. The in situ microelectromechanical systems’ (MEMS) sensors can provide high time and spatial resolution of weather parameter measurement, but they suffer from stochastic measurement error. In order to apply MEMS sensors in real-time rainfall prediction in Wuhan, firstly, seasonal trend decomposition using Loess (STL) algorithm is utilized to decompose the observed time series into trend, seasonal, and remainder components. The trend of the observed series is compared with the corresponding trend of the data downloaded from the authoritative website with the same weather parameter in terms of Euclidean distance and cosine similarity. The similarity demonstrates that the observation of MEMS sensors is believable. Secondly, the long short-term memory (LSTM) is used to predict the real-time rainfall based on the observed data. Compared with autoregressive and moving average (ARMA), random forest (RF), support vector machine (SVM), and back propagation neural networks (BPNNs), LSTM not only performs as well as ARMA in real-time rainfall prediction but also outperforms the other four models in seasonal rainfall pattern description and seasonal real-time rainfall prediction. Our experiment results show that more detailed, timely, and accurate rainfall prediction can be achieved by using LSTM on the MEMS weather sensors.

Highlights

Rainfall is one of the major severe phenomena within a climate system
The long short-term memory (LSTM) was used by two manners to predict rainfall
The developed automatic meteorological stations based on microelectromechanical systems’ (MEMS) sensors can provide high time resolution and spatial resolution weather monitoring

Summary

Introduction

Rainfall is one of the major severe phenomena within a climate system. It has a direct influence on water resource management, agriculture, and ecosystems [1]. After obtaining weather sensor data for more than 3 years in Wuhan, we wish to demonstrate that the MEMS sensor observation is believable and the data are available to real-time rainfall prediction. It is assumed that the observed sensor time series have trend, seasonal, and residual components. The models of machine learning algorithms have been applied to solve the real-time rainfall prediction problem. We choose the LSTM to predict real-time rainfall based on the observed sensor data at Wuhan University. The experiment results show that LSTM is better to describe the seasonal rainfall process than ARMA, RF, SVM, and BPNNs. LSTM is found more suitable for the sensor time series that usually have missing data. (i) The weather time series sampled by WSN with 5-minute interval have been proved to be effective and valuable in local meteorological monitoring.

Weather Data Observed by a Wireless Sensor Network

Proof of Validity of the Observed Time Series

Local Real-Time Rainfall Prediction with LSTM

Results

Conclusion